Non-parametric & Rank Correlation

Compare monotonic and linear association through Spearman and Kendall methods. Assign average ranks, count concordant and discordant pairs, compute untied and tied examples, and assess transformation invariance and sensitivity. Prerequisites: Pearson correlation and elementary counting. Ties and zero rank variance are handled explicitly; advanced inference is deferred. Includes single-select and multiselect practice with worked explanations and misconception-specific feedback.

Further reading and method references:

• SciPy: Spearman correlation
• SciPy: Kendall correlation